






'''simple docstring'''





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Union[str, Any]  =					{'vocab_file': 'spiece.model'}

_UpperCamelCase						:						Tuple  =					{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

_UpperCamelCase						:						str  =					{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
_UpperCamelCase						:						List[str]  =					0
_UpperCamelCase						:						List[str]  =					1
_UpperCamelCase						:						Tuple  =					2
_UpperCamelCase						:						Tuple  =					3
_UpperCamelCase						:						Optional[Any]  =					4


class  snake_case__      (					UpperCamelCase):
					a_        =					VOCAB_FILES_NAMES
					a_        =					PRETRAINED_VOCAB_FILES_MAP
					a_        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_        =					"left"




					def __init__(  self  :		Dict				, _A  :		Optional[Any]				, _A  :		List[Any]=False				, _A  :		Optional[int]=True				, _A  :		List[Any]=False				, _A  :		str="<s>"				, _A  :		Union[str, Any]="</s>"				, _A  :		Tuple="<unk>"				, _A  :		str="<sep>"				, _A  :		int="<pad>"				, _A  :		Optional[int]="<cls>"				, _A  :		Optional[Any]="<mask>"				, _A  :		List[Any]=["<eop>", "<eod>"]				, _A  :		Optional[Dict[str, Any]] = None				, **_A  :		int				, )    ->							None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase_      :       Optional[int]          =       AddedToken(_A				, lstrip=_A				, rstrip=_A      ) if isinstance(_A				, _A      ) else mask_token

								UpperCAmelCase_      :       str          =       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=_A				, remove_space=_A				, keep_accents=_A				, bos_token=_A				, eos_token=_A				, unk_token=_A				, sep_token=_A				, pad_token=_A				, cls_token=_A				, mask_token=_A				, additional_special_tokens=_A				, sp_model_kwargs=self.sp_model_kwargs				, **_A				, )

								UpperCAmelCase_      :       List[Any]          =       3

								UpperCAmelCase_      :       Union[str, Any]          =       do_lower_case
								UpperCAmelCase_      :       Tuple          =       remove_space
								UpperCAmelCase_      :       Any          =       keep_accents
								UpperCAmelCase_      :       Optional[Any]          =       vocab_file

								UpperCAmelCase_      :       Tuple          =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(_A      )




					@property
					def 		A      (  self  :		Dict      )    ->							int:
								return len(self.sp_model      )




					def 		A      (  self  :		List[Any]      )    ->							Dict:
								UpperCAmelCase_      :       Dict          =       {self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab




					def __getstate__(  self  :		List[str]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       List[Any]          =       self.__dict__.copy()
								UpperCAmelCase_      :       Dict          =       None
								return state




					def __setstate__(  self  :		List[Any]				, _A  :		Optional[int]      )    ->							Dict:
								UpperCAmelCase_      :       List[str]          =       d

								# for backward compatibility
								if not hasattr(self				, '''sp_model_kwargs'''      ):
											UpperCAmelCase_      :       List[str]          =       {}

								UpperCAmelCase_      :       int          =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(self.vocab_file      )




					def 		A      (  self  :		List[str]				, _A  :		str      )    ->							Tuple:
								if self.remove_space:
											UpperCAmelCase_      :       int          =       ''' '''.join(inputs.strip().split()      )
								else:
											UpperCAmelCase_      :       List[Any]          =       inputs
								UpperCAmelCase_      :       Optional[Any]          =       outputs.replace('''``'''				, '''"'''      ).replace('''\'\''''				, '''"'''      )

								if not self.keep_accents:
											UpperCAmelCase_      :       Tuple          =       unicodedata.normalize('''NFKD'''				, _A      )
											UpperCAmelCase_      :       Tuple          =       ''''''.join([c for c in outputs if not unicodedata.combining(_A      )]      )
								if self.do_lower_case:
											UpperCAmelCase_      :       Tuple          =       outputs.lower()

								return outputs




					def 		A      (  self  :		List[str]				, _A  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.preprocess_text(_A      )
								UpperCAmelCase_      :       Optional[Any]          =       self.sp_model.encode(_A				, out_type=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       []
								for piece in pieces:
											if len(_A      ) > 1 and piece[-1] == str(''','''      ) and piece[-2].isdigit():
														UpperCAmelCase_      :       Optional[int]          =       self.sp_model.EncodeAsPieces(piece[:-1].replace(_A				, ''''''      )      )
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																	if len(cur_pieces[0]      ) == 1:
																				UpperCAmelCase_      :       Optional[Any]          =       cur_pieces[1:]
																	else:
																				UpperCAmelCase_      :       Any          =       cur_pieces[0][1:]
														cur_pieces.append(piece[-1]      )
														new_pieces.extend(_A      )
											else:
														new_pieces.append(_A      )

								return new_pieces




					def 		A      (  self  :		List[str]				, _A  :		Tuple      )    ->							str:
								return self.sp_model.PieceToId(_A      )




					def 		A      (  self  :		Tuple				, _A  :		List[str]      )    ->							str:
								return self.sp_model.IdToPiece(_A      )




					def 		A      (  self  :		int				, _A  :		Dict      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Any          =       ''''''.join(_A      ).replace(_A				, ''' '''      ).strip()
								return out_string




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[int]				, _A  :		bool = False				, _A  :		bool = None				, _A  :		bool = True				, **_A  :		Dict				, )    ->							str:
								UpperCAmelCase_      :       List[str]          =       kwargs.pop('''use_source_tokenizer'''				, _A      )

								UpperCAmelCase_      :       Union[str, Any]          =       self.convert_ids_to_tokens(_A				, skip_special_tokens=_A      )

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								UpperCAmelCase_      :       str          =       []
								UpperCAmelCase_      :       List[Any]          =       []
								for token in filtered_tokens:
											if skip_special_tokens and token in self.all_special_ids:
														continue
											if token in self.added_tokens_encoder:
														if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(_A      )      )
																	UpperCAmelCase_      :       List[Any]          =       []
														sub_texts.append(_A      )
											else:
														current_sub_text.append(_A      )
								if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(_A      )      )

								# Mimic the behavior of the Rust tokenizer:
								# By default, there are no spaces between special tokens
								UpperCAmelCase_      :       Tuple          =       ''''''.join(_A      )

								UpperCAmelCase_      :       List[Any]          =       (
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
											UpperCAmelCase_      :       Optional[Any]          =       self.clean_up_tokenization(_A      )
											return clean_text
								else:
											return text




					def 		A      (  self  :		int				, _A  :		List[int]				, _A  :		Optional[List[int]] = None      )    ->							List[int]:
								UpperCAmelCase_      :       Union[str, Any]          =       [self.sep_token_id]
								UpperCAmelCase_      :       Optional[Any]          =       [self.cls_token_id]
								if token_ids_a is None:
											return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls




					def 		A      (  self  :		Tuple				, _A  :		List[int]				, _A  :		Optional[List[int]] = None				, _A  :		bool = False      )    ->							List[int]:

								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_A				, token_ids_a=_A				, already_has_special_tokens=_A      )

								if token_ids_a is not None:
											return ([0] * len(_A      )) + [1] + ([0] * len(_A      )) + [1, 1]
								return ([0] * len(_A      )) + [1, 1]




					def 		A      (  self  :		Optional[int]				, _A  :		List[int]				, _A  :		Optional[List[int]] = None      )    ->							List[int]:
								UpperCAmelCase_      :       List[Any]          =       [self.sep_token_id]
								UpperCAmelCase_      :       Any          =       [2]

								if token_ids_a is None:
											return len(token_ids_a + sep      ) * [0] + cls_segment_id
								return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id





					def 		A      (  self  :		List[str]				, _A  :		str				, _A  :		Optional[str] = None      )    ->							Tuple[str]:
								if not os.path.isdir(_A      ):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
											return
								UpperCAmelCase_      :       Optional[int]          =       os.path.join(
								    _A				, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(_A      ) and os.path.isfile(self.vocab_file      ):
											copyfile(self.vocab_file				, _A      )
								elif not os.path.isfile(self.vocab_file      ):
											with open(_A				, '''wb'''      ) as fi:
														UpperCAmelCase_      :       int          =       self.sp_model.serialized_model_proto()
														fi.write(_A      )

								return (out_vocab_file,)










'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


